soft_prompt/tasks/ner/datasets/conll2004.py

# coding=utf-8
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# Lint as: python3
"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
    title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
    author = "Tjong Kim Sang, Erik F.  and
      De Meulder, Fien",
    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
    year = "2003",
    url = "https://www.aclweb.org/anthology/W03-0419",
    pages = "142--147",
}
"""

_DESCRIPTION = """\
The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
not belong to the previous three groups.
The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2
tagging scheme, whereas the original dataset uses IOB1.
For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
"""

_URL = "../../../data/CoNLL04/"
_TRAINING_FILE = "train.txt"
_DEV_FILE = "dev.txt"
_TEST_FILE = "test.txt"


class CoNLL2004Config(datasets.BuilderConfig):
    """BuilderConfig for CoNLL2004"""

    def __init__(self, **kwargs):
        """BuilderConfig for CoNLL2004 5.0.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(CoNLL2004Config, self).__init__(**kwargs)


class CoNLL2004(datasets.GeneratorBasedBuilder):
    """CoNLL2004 dataset."""

    BUILDER_CONFIGS = [
        CoNLL2004Config(name="CoNLL2004", version=datasets.Version("1.0.0"), description="CoNLL2004 dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names= ['O', 'B-Loc', 'B-Peop', 'B-Org', 'B-Other', 'I-Loc', 'I-Peop', 'I-Org', 'I-Other']
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://catalog.ldc.upenn.edu/LDC2013T19",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            "dev": f"{_URL}{_DEV_FILE}",
            "test": f"{_URL}{_TEST_FILE}",
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            ner_tags = []
            for line in f:
                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                else:
                    # OntoNotes 5.0 tokens are space separated
                    splits = line.split(" ")
                    tokens.append(splits[0])
                    ner_tags.append(splits[-1].rstrip())